Yet, DL-based generative models are still at a nascent stage for scientific principles. Applications in molecular and materials discovery have yet to be utilized in wide applications. Generative models of chemical structures especially have yet to experience similar growth as in image and sequence generation. This can be largely traced to the difficulty in identifying a common representation for chemical structures. Molecules and materials cover a large set of possible combinations of atomic elements with varying structures, and varying representations, each with its own strengths and drawbacks. Some classes of materials such as molecules have textual representations such as SMILES [1]} and SELFIES [2]}. The same molecules are also succinctly represented using graphs and graph-based structures such as coulomb matrices  [3]}, [4]}. Materials such as crystals on the other hand do not share such representations. The difficulty in the representation of these materials is further compounded due to the spatial symmetry requirements and periodicity. We can see the various representations of molecular structures in Fig. REF .
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